Tissue rejection is the principal cause of heart transplant failures, occurring when a recipient's immune system attacks the transplanted heart. Suppressing this immune system response is critical to the success of heart transplants. Pharmaceutical agents such as Cyclosporine A (CSA), steroids and Azathioprine are used to control and suppress a recipient's immune system response to grafted tissue. However, suppressing a recipient's immune system renders him more vulnerable to infection. Adjusting immune system suppression to the minimum required is thus a major objective. To do this, the transplanted heart tissue must be monitored continuously and carefully for signs of rejection.
Recognizing the onset and severity of rejection is a major problem. Tissue rejection in heart transplant recipients is generally silent until the heart is damaged irreversibly. It is crucial to decide whether or not a patient is rejecting his or her transplanted heart, so that potentially life saving therapy can be started immediately. Thus, early and reliable detection of graft rejection is vital to the success of heart transplants.
At present, the only reliable method for diagnosing rejection requires frequent endomycardial biopsy (EMBx), an expensive (roughly $1,200 or more per procedure), invasive procedure that must be performed by a sub-specialist. The biopsy is studied by a pathologist for the invasion of heart tissue by white blood cells, edema, and dead cardiac muscle cells--the histologic manifestations of rejection. In 85% of cases histologic diagnosis determines treatment for rejection; treatment is determined by clinical judgment in only 15% of cases. Although EMBx is associated with a morbidity of 1-4% in experienced hands, the need for repeated, invasive procedures adds significantly to cost and patient discomfort during post transplant follow-up. A reliable, non-invasive method for detecting rejection is thus needed.
A number of investigators have used a decrease in voltage amplitude on the electrocardiogram (EKG) as a marker for rejection. In the early days of transplantation, a decrease in voltage on the surface EKG correlated well with rejection. With the introduction of CSA in 1982 for immuno-suppression, voltage measurements from surface EKGs became unreliable. More recently, voltage amplitudes from intramyocardial screw-in electrodes have been used to diagnose rejection, as have T-wave amplitudes produced from ventricular pacing. Although these techniques are sensitive and specific for detecting rejection, they require permanent implantation of hardware in the patient's body.
Several investigators have analyzed the EKG using the fast Fourier transform (FFT). FFT measures the spectral power of heart over a range of frequencies. Because the heart rate time series is extremely complicated, frequencies with low statistical weight may be lost because they are indistinguishable from noise. Thus, this approach has not provided a reliable indicator of rejection.
Accordingly, there remains a need for a reliable, non-invasive method for detecting rejection. The present invention provides such a method, and corresponding apparatus, wherein rejection is diagnosed based on the dynamics of heart beat rhythm.
Since the time of Galen, examination of the pulse has been a time-honored ritual in examining a patient. Physicians have learned that dramatic changes in cardiac rhythm may reveal fundamental changes in the health of the heart. The tradition of examining the pulse forms the basis for the novel technique of the present invention for diagnosing rejection in heart transplant recipients. Unlike traditional methods of heart beat analysis based on "gross" variations in heart beat rhythm, the present invention employs the principles of dynamical systems theory to diagnose changes in the health of the heart based on variations which are imperceptible without the use of high precision electronic measurements and computer-aided analysis. These changes from one day to the next may indicate in the heart transplant recipient that rejection has begun.
The present invention uses the investigative tools of dynamical systems analysis to characterize the heart's reaction to rejection. Dynamical systems theory seeks to classify system behavior into one of three classes: 1) steady state; 2) periodic; and 3) chaotic. The theory raises the possibility that seemingly complex unpredictable behavior may be explained by simple deterministic rules. According to P. E. Rapp, in his paper entitled "Chaos in the Neurosciences: Cautionary Tales from the Frontier" (unpublished at the time of filing of this application), the study of chaos in the laboratory is fraught with great difficulty. All measurements contain noise. Differentiating between random noise and deterministic chaos is difficult at best. Worse yet, plausible yet totally spurious results can be obtained from measurements which are largely noise. Therefore, applying the methods of dynamical systems theory to clinical diagnosis remains at the edge of science, and the results of such studies are still viewed with skepticism.
To date, there are a number of publications reporting studies of the dynamics of the heart rhythm, including chaotic dynamics. One general study of heart rate dynamics is reported by Kleiger, et al., in "Decreased Heart Rate Variability and its Association with Increased Mortality after Acute Myocardial Infarction," Am J Cardiol, 59:256-262 (1987) Kleiger et al. report that heart rate variability can be decreased with severe coronary artery disease, congestive heart failure, aging and diabetic neuropathy. Casolo et al. reported similar results for congestive heart failure in the paper entitled "Decreased Spontaneous Heart Rate Variability in Congestive Heart Failure," Am J Cardiol, 64:1162-1167 (1989). Data in these studies were analyzed using only means and standard deviations.
Anan et al. describe a more sophisticated analytical approach in their study "Arrhythmia Analysis by Successive RR Plotting," J. Electrocardiol 23:243-248 (1990). In this paper, the authors looked at the coupling interval-dependent characteristics of arrhythmia based on the gross behavior of heart rhythm, and found that RR interval plotting using data created by the method of delays could be useful both in detecting and in highlighting specific features of various types of arrhythmia, based on the gross behavior of heart rhythm.
Also published in 1990 is a paper by Chialvo et al. entitled "Low dimension chaos in cardiac tissue," Nature, 343:653-657 (1990). This paper reported experimental evidence for chaotic patterns of activation and action potential characteristics in externally driven, non-spontaneously active Purkinje fibers and ventricular muscle. Chialvo et al. did not investigate clinical applications of chaos in cardiac tissue, but restricted their investigation to the action potentials of cells in a petri dish.
A study on the chaotic dynamics of the heart published by Skinner et al. entitled "Chaos in the Heart: Implications for Clinical Radiology," Bio/Technology, 8:1018-1024 (1990), reports that chaos appears to occur in the heart beat time-series. Skinner, et al. discussed in the possible correlation of chaos in heart rhythm with arrythmia, ischemia, myocardial infarction, CHF and old age. In a study of a pig subjected to progressively reduced coronary blood flow, Skinner et al. found that ischemia appeared to produce a decrease in the dimensional complexity of the heart beat as compared to normal.
Another known publication is entitled "Dimensional Analysis of Heart Rate Variability in Heart Transplant Recipients," by Zbilut et al., Mathematical Biosciences 90:49-70 (1988). This paper reports what appears to be a general study of the chaotic dynamics of denervated transplanted hearts. It appears that the primary intent of the study is to test several algorithms for computing dimension on clinical data. The study concludes that there is an apparent reduction of heart rhythm "dimensionality" (a measure of the complexity of a system's behavior in chaos theory) with rejection. However, the paper is inconclusive on this point: "Since we do not claim statistically convincing results at this point, we did not systematically study our dimension estimates for different subsets of our data set" (p. 66). In fact, upon close inspection the clinical results obtained by the study are easily interpreted to suggest that no clear correlation between rejection and reduced dimensionality exists. In particular, based on the study of four transplant patients, Zbilut et al. report that a decrease in the singular-value decomposition estimate of dimension, D-SV, may signal rejection. D-SV for the three non-rejecting patients studied ranges from 2.6-3.6, with standard deviations approaching half of the mean values. However, D-SV for the single episode of rejection equalled 2.9, which falls squarely within their defined "normal" range. Clearly, this result supports the conclusion that there is no clear correlation between a reduction in dimensionality and rejection.
There are several other deficiencies and aspects of the results reported by Zbilut et al. that render their results inconclusive, at least as to the usefulness of dynamical systems theory to diagnose rejection of heart tissue. For instance, there is no indication in the paper as to the level of precision at which the heart beat intervals are measured. Moreover, the phase plots presented in the paper do not appear to differ qualitatively between rejecting and non-rejecting hearts (FIG. 7 vs. FIG. 8), again countering any suggestion that dynamical systems can be correlated with rejection in a transplanted heart. Therefore, the results reported by Zbilut et al. are inconclusive at best, and in fact can be fairly characterized as demonstrating a lack of correlation between rejection and a change in the behavior of heart rhythm.